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Jupyter Agents: training LLMs to reason with notebooks

Hugging Face dropped an open pipeline and dataset for training small models—think **Qwen3-4B**—into sharp **Jupyter-native data science agents**. They pulled curated Kaggle notebooks, whipped up synthetic QA pairs, added lightweight **scaffolding**, and went full fine-tune. Net result? A **36% jump .. read more  

Jupyter Agents: training LLMs to reason with notebooks
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Building a Natural Language Interface for Apache Pinot with LLM Agents

MiQ plugged **Google’s Agent Development Kit** into their stack to spin up **LLM agents** that turn plain English into clean, validated SQL. These agents speak directly to **Apache Pinot**, firing off real-time queries without the usual parsing pain. Behind the scenes, it’s a slick handoff: NL2SQL .. read more  

Building a Natural Language Interface for Apache Pinot with LLM Agents
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Implementing Vector Search from Scratch: A Step-by-Step Tutorial

Search is a fundamental problem in computing, and vector search aims to match meanings rather than exact words. By converting queries and documents into numerical vectors and calculating similarity, vector search retrieves contextually relevant results. In this tutorial, a vector search system is bu.. read more  

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5 Free AI Courses from Hugging Face

Hugging Face just rolled out a sharp set of free AI courses. Real topics, real tools—think **AI agents, LLMs, diffusion models, deep RL**, and more. It’s hands-on from the jump, packed with frameworks like LangGraph, Diffusers, and Stable Baselines3. You don’t just read about models—you build ‘em i.. read more  

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Inside NVIDIA GPUs: Anatomy of high performance matmul kernels

NVIDIA Hopper packs serious architectural tricks. At the core: **Tensor Memory Accelerator (TMA)**, **tensor cores**, and **swizzling**—the trio behind async, cache-friendly matmul kernels that flirt with peak throughput. But folks aren't stopping at cuBLAS. They're stacking new tactics: **warp-gro.. read more  

Inside NVIDIA GPUs: Anatomy of high performance matmul kernels
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Becoming a Research Engineer at a Big LLM Lab - 18 Months of Strategic Career Development

To land a big career role like Mistral, mix efficient **tactical** moves (like LeetCode practice) with **strategic** ups, like building a powerful portfolio and a solid network. Balance is key; aim to impress and prepare well without overlooking the power of strategy in shaping a successful career... read more  

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Shai-Hulud npm Supply Chain Attack

Malicious npm packages just leveled up: this one dropped a self-spreading worm that hijacks repos and leaks secrets the moment it lands. It abuses `postinstall` scripts to run TruffleHog and swipe tokens straight from your codebase. Then it uses GitHub Actions to exfiltrate the loot and auto-publis.. read more  

Shai-Hulud npm Supply Chain Attack
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How FinOps Drives Value for Every Engineering Dollar

Duolingo’s FinOps crew didn’t just track cloud costs—they wired up sharp, automated observability across 100+ microservices. Real-time alerts now catch AI and infra spend spikes before they torch the budget. They sliced TTS costs by 40% with in-memory caching. Dumped pricey CloudWatch metrics for P.. read more  

How FinOps Drives Value for Every Engineering Dollar
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@faun shared a link, 7 months ago
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Demystifying Log Retention in Azure

Azure logs come in three flavors: **Activity Logs**, **Diagnostic Logs**, and **Log Analytics**. Each with its own rules for retention and billing. The catch? Those differences aren’t quirks—they’re baked in... read more  

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Observability for the Invisible: Tracing Message Drops in Kafka Pipelines

When an event drops silently in a distributed system, it is not a bug, it is an architectural blind spot. Detect, debug, and prevent message loss in Kafka-based streaming pipelines using tools like OpenTelemetry, Fluent Bit, Jaeger, and dead-letter queues. Make sure observability gaps in event strea.. read more  

FAUN.dev() is a developer-first platform built with a simple goal: help engineers stay sharp without wasting their time. It curates practical newsletters, thoughtful technical blogs, and useful developer tools that focus on signal over noise.

Created by engineers, for engineers, FAUN.dev() is where experienced developers turn to keep up with the fast-moving world of DevOps, Kubernetes, Cloud Native, AI, and modern programming. We handpick what matters and skip the fluff.

If it’s on FAUN.dev(), it’s worth your attention.

Beyond curation, we run a course marketplace (WIP) designed to keep developers current. These courses go deep into the subjects that shape real-world work—things like Kubernetes internals, modern DevOps workflows, cloud-native architecture, and using AI tools to build faster and smarter. It’s practical learning, taught by people who’ve done the work. Developers from companies like GitHub, Netflix, and Shopify already rely on FAUN.dev() to stay on top of their game. They trust us because we keep it real: no hype, no filler, just what you need to grow and do your best work. For sponsors and partners, FAUN.dev() offers access to a focused, engaged audience of technical professionals. This isn’t just another broad developer community—it’s a place where smart engineers go to get smarter. If you have something meaningful to offer them, you’ll be in good company. In short, FAUN.dev() is more than a content hub. It’s a place to grow, to learn, and to connect with what really matters in software today.